Network capacity planning considering the control plane bottleneck

ABSTRACT

Aspects of the subject disclosure may include, for example, obtaining first information indicative of data plane utilization, wherein the data plane is associated with a wireless communications network; obtaining second information indicative of control plane utilization, wherein the control plane is associated with the wireless communications network; applying the first information and the second information to one or more machine learning algorithms; and generating via the one or more machine learning algorithms one or more outputs, wherein the one or more outputs indicates whether one or more network resources should be added to improve the data plane utilization. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application claims the benefit of priority to U.S. Provisional Application No. 63/315,263, filed Mar. 1, 2022. All sections of the aforementioned application(s) and/or patent(s) are incorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to network capacity planning considering the control plane bottleneck.

BACKGROUND

The control plane and the data plane are two basic components of a telecommunications architecture. The control plane carries signaling traffic and serves the data plane, and the data plane bears the traffic that the network exists to carry.

The control plane is responsible for all signaling events, including different traffic types (voice, data, broadcasting, paging, controlling, etc.), and different transmission directions (downlink and uplink).

In general, a relatively small amount of network resources will typically be allocated for control channels, with the majority of the resources being utilized for the data plane. However, when there are a lot of events happening, it is possible that the control plane becomes the bottleneck of the network. In that case, the resources allocated for the data plane will be inefficiently utilized and the overall performance degrades.

The control plane resource utilization is difficult to model. Since the control plane resource utilization is dealing with signaling traffic (which has different characteristics than normal data traffic) there are very few recognized methods that can handle such control plane resource utilization. To add to the complexity of modeling, in the wireless networks, the amount of resources utilized by each traffic type and each direction depends on the signal strength of the users in the system.

Various conventional network capacity planning tools mainly focus on the data plane resource utilization (partially because there exist known methods like communication theories, queueing theories, or simulations) that can model the system. Due to various complexity challenges, the control plane has been largely ignored in various conventional dimensioning tools (although some simple conventional regression models can be utilized to perform numerical analysis).

U.S. Pat. Application Publication 2021/0352517, filed on May 7, 2020 (Applicant: AT&T Intellectual Property I, L.P.; Inventors: Huahui Wang, et al.) relates to APPARATUSES AND METHODS TO FACILITATE LOAD-AWARE RADIO ACCESS NETWORK RESOURCE ALLOCATIONS. All sections of this document are incorporated herein by reference in their entirety.

U.S. Pat. 11,218,888, filed on May 7, 2020 (Applicant: AT&T Intellectual Property I, L.P.; Inventors: Gopalakrishnan Meempat, et al.) relates to APPARATUSES AND METHODS FOR NETWORK RESOURCE DIMENSIONING IN ACCORDANCE WITH DIFFERENTIATED QUALITY OF SERVICE. All sections of this document are incorporated herein by reference in their entirety

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an example, non-limiting embodiment of a communication network in accordance with various aspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limiting embodiment of a system architecture (which can function fully or partially within the communication network of FIG. 1 ) in accordance with various aspects described herein.

FIG. 2B is a block diagram illustrating an example, non-limiting embodiment of key training features in accordance with various aspects described herein.

FIGS. 2C-2D are block diagrams illustrating example, non-limiting embodiments directed to model & prediction accuracy in accordance with various aspects described herein.

FIGS. 2E-2F are block diagrams illustrating example, non-limiting embodiments directed to determining threshold for control plane congestion in accordance with various aspects described herein.

FIG. 2G depicts an illustrative embodiment of a method in accordance with various aspects described herein.

FIG. 2H depicts an illustrative embodiment of a method in accordance with various aspects described herein.

FIG. 2I depicts an illustrative embodiment of a method in accordance with various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limiting embodiment of a virtualized communication network in accordance with various aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of a computing environment in accordance with various aspects described herein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of a mobile network platform in accordance with various aspects described herein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of a communication device in accordance with various aspects described herein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrative embodiments for network capacity planning considering the control plane bottleneck. Other embodiments are described in the subject disclosure.

One or more aspects of the subject disclosure include network planning (and/or configuration) mechanisms that are based upon both data plane utilization and control plane utilization.

One or more aspects of the subject disclosure include a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining first information indicative of data plane utilization, wherein the data plane is associated with a wireless communications network; obtaining second information indicative of control plane utilization, wherein the control plane is associated with the wireless communications network; applying the first information and the second information to one or more machine learning algorithms; and generating via the one or more machine learning algorithms one or more outputs, wherein the one or more outputs indicates whether one or more network resources should be added to improve the data plane utilization.

One or more aspects of the subject disclosure include a non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: receiving first data associated with a first frequency band of a wireless network, wherein the first data characterizes utilization of a data plane of the wireless network at the first frequency band; receiving second data associated with a second frequency band of the wireless network, wherein the second data characterizes utilization of the data plane of the wireless network at the second frequency band, and wherein the second frequency band is different from the first frequency band; receiving third data associated with the first frequency band of the wireless network, wherein the third data characterizes utilization of a control plane of the wireless network at the first frequency band; receiving fourth data associated with the second frequency band of the wireless network, wherein the fourth data characterizes utilization of the control plane of the wireless network at the second frequency band; applying the first data and the third data to a first machine learning algorithm; generating via the first machine learning algorithm one or more first outputs, wherein the one or more first outputs indicates whether one or more first network resources associated with the first frequency band should be adjusted to improve data plane utilization at the first frequency band; applying the second data and the fourth data to a second machine learning algorithm, wherein the second machine learning algorithm is different from the first machine learning algorithm; and generating via the second machine learning algorithm one or more second outputs, wherein the one or more second outputs indicates whether one or more second network resources associated with the second frequency band should be adjusted to improve data plane utilization at the second frequency band.

One or more aspects of the subject disclosure include a method, comprising: obtaining, by a processing system including a processor, first training data associated with a first frequency band of a wireless network, wherein the first training data characterizes utilization of a data plane of the wireless network at the first frequency band; obtaining, by the processing system, second training data associated with a second frequency band of the wireless network, wherein the second training data characterizes utilization of the data plane of the wireless network at the second frequency band, and wherein the second frequency band is distinct from the first frequency band; obtaining, by the processing system, third training data associated with the first frequency band of the wireless network, wherein the third training data characterizes utilization of a control plane of the wireless network at the first frequency band; obtaining, by the processing system, fourth training data associated with the second frequency band of the wireless network, wherein the fourth training data characterizes utilization of the control plane of the wireless network at the second frequency band; utilizing, by the processing system, the first training data and the third training data to train a first machine learning algorithm that is associated with the first frequency band; and utilizing, by the processing system, the second training data and the fourth training data to train a second machine learning algorithm that is associated with the second frequency band, wherein the second machine learning algorithm is distinct from the first machine learning algorithm.

As described herein, control plane dimensioning has traditionally been challenging because there has typically been no known methodology to accurately calculate the resource utilization (due to the complexity of the traffic carried in the control channel). In contrast, various embodiments provide for a comprehensive network planning system by including both the data plane and the control plane in the process. In various examples, the challenge of the control plane resource utilization is addressed by using machine learning algorithms with carefully selected training features.

One or more aspects of the subject disclosure include an approach for control plane dimensioning. For the purposes of the discussion herein, certain wireless networks (such as LTE/5G) are used as examples to illustrate how to use machine learning models to calculate the control plane resource utilization. In one example, the most critical step for success is to determine the essential features that impact the control channel resource utilization. After such key features are determined, field data can be collected from the real network, and can be processed as a training data set and a testing data set. The training data set can be used to train machine learning models, and the validation can be performed using the testing data set. The model can be updated/refreshed periodically using new data from the field. Engineering thresholds for control plane capacity triggers can be determined using correlation analysis (i.e., to derive the performance vs. control plane resource utilization relation). To predict the control plane resource utilization in the future, the features to the machine learning model can be predicted first using existing tools (for example, such as described in U.S. Pat. Application Publication 2021/0352517, entitled APPARATUSES AND METHODS TO FACILITATE LOAD-AWARE RADIO ACCESS NETWORK RESOURCE ALLOCATIONS), and then the trained model can be used to calculate control plane resource utilization. In one example, if the predicted utilization is higher than the engineering threshold, it is determined that new capacity is required in the planning process.

One or more aspects of the subject disclosure include an approach directed to the PDCCH channel (Physical Downlink Control Channel) for LTE/5G (control plane). In this regard, it is noted that the PDCCH channel is used for:

-   Downlink (DL) resource allocation -   Uplink (UL) grants -   PRACH (Physical Random Access Channel) responses -   UL power control commands -   Common scheduling assignments for signaling messages (such as system     information, paging, etc.).

Further, it is noted that in a PDCCH LTE example:

-   Up to the first 3 OFDM symbols per TTI are generally used for PDCCH -   PDCCH is made up of CCEs (Control Channel Elements) -   Each CCE is made up of 36 REs (Resource Elements) -   PDCCH further uses a concept of aggregation layers which is a group     of CCEs. -   There are 4 aggregation levels (1,2,4,8) in the normal PDCCH

In other examples, NR (5G New Radio) can have similar but different parameters, defined by 3GPP.

Further still, it is noted that Utilization can comprise:

-   Number/frequency of events -   Types of traffic (voice, or data, etc.) -   Both DL and UL directions -   UE RF conditions (CCE aggregation levels)

Referring now to FIG. 1 , a block diagram is shown illustrating an example, non-limiting embodiment of a system 100 in accordance with various aspects described herein. For example, system 100 can facilitate in whole or in part network planning (and/or configuration) mechanisms that are based upon both data plane utilization and control plane utilization. In particular, a communications network 125 is presented for providing broadband access 110 to a plurality of data terminals 114 via access terminal 112, wireless access 120 to a plurality of mobile devices 124 and vehicle 126 via base station or access point 122, voice access 130 to a plurality of telephony devices 134, via switching device 132 and/or media access 140 to a plurality of audio/video display devices 144 via media terminal 142. In addition, communication network 125 is coupled to one or more content sources 175 of audio, video, graphics, text and/or other media. While broadband access 110, wireless access 120, voice access 130 and media access 140 are shown separately, one or more of these forms of access can be combined to provide multiple access services to a single client device (e.g., mobile devices 124 can receive media content via media terminal 142, data terminal 114 can be provided voice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements (NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110, wireless access 120, voice access 130, media access 140 and/or the distribution of content from content sources 175. The communications network 125 can include a circuit switched or packet switched network, a voice over Internet protocol (VoIP) network, Internet protocol (IP) network, a cable network, a passive or active optical network, a 4G, 5G, or higher generation wireless access network, WIMAX network, UltraWideband network, personal area network or other wireless access network, a broadcast satellite network and/or other communications network.

In various embodiments, the access terminal 112 can include a digital subscriber line access multiplexer (DSLAM), cable modem termination system (CMTS), optical line terminal (OLT) and/or other access terminal. The data terminals 114 can include personal computers, laptop computers, netbook computers, tablets or other computing devices along with digital subscriber line (DSL) modems, data over coax service interface specification (DOCSIS) modems or other cable modems, a wireless modem such as a 4G, 5G, or higher generation modem, an optical modem and/or other access devices.

In various embodiments, the base station or access point 122 can include a 4G, 5G, or higher generation base station, an access point that operates via an 802.11 standard such as 802.11n, 802.11ac or other wireless access terminal. The mobile devices 124 can include mobile phones, e-readers, tablets, phablets, wireless modems, and/or other mobile computing devices.

In various embodiments, the switching device 132 can include a private branch exchange or central office switch, a media services gateway, VoIP gateway or other gateway device and/or other switching device. The telephony devices 134 can include traditional telephones (with or without a terminal adapter), VoIP telephones and/or other telephony devices.

In various embodiments, the media terminal 142 can include a cable head-end or other TV head-end, a satellite receiver, gateway or other media terminal 142. The display devices 144 can include televisions with or without a set top box, personal computers and/or other display devices.

In various embodiments, the content sources 175 include broadcast television and radio sources, video on demand platforms and streaming video and audio services platforms, one or more content data networks, data servers, web servers and other content servers, and/or other sources of media.

In various embodiments, the communications network 125 can include wired, optical and/or wireless links and the network elements 150, 152, 154, 156, etc. can include service switching points, signal transfer points, service control points, network gateways, media distribution hubs, servers, firewalls, routers, edge devices, switches and other network nodes for routing and controlling communications traffic over wired, optical and wireless links as part of the Internet and other public networks as well as one or more private networks, for managing subscriber access, for billing and network management and for supporting other network functions.

As described herein, various embodiments can provide a method and an apparatus for network planning. The method and apparatus can consider both data plane and control plane capacity requirements.

As described herein, various embodiments can provide a method and an apparatus that re-utilizes the data plane outcomes as inputs to the control plane decisioning and predicts the control plane resource utilization.

As described herein, various embodiments can provide a method and an apparatus that utilize a set of key features that can be used in order to accurately predict the control plane resource utilization.

As described herein, various embodiments can provide a method and an apparatus that use machine learning algorithms to predict control plane utilization.

As described herein, various embodiments can provide a method and an apparatus that empirically determine the threshold which indicates congestion of the control plane.

Referring now to FIG. 2A, this is a block diagram illustrating an example, non-limiting embodiment of a system architecture 200 (which can function fully or partially within the communication network of FIG. 1 ) in accordance with various aspects described herein. In general, this figure shows an architecture directed to network dimensioning with both data plane and control plane considerations.

Still referring to FIG. 2A, it is seen that the Data Plane Dimensioning block 204 receives various inputs from the Input block 202. In one example, the Data Plane Dimensioning block 204 can be as described in U.S. Pat. Application Publication 2021/0352517, entitled APPARATUSES AND METHODS TO FACILITATE LOAD-AWARE RADIO ACCESS NETWORK RESOURCE ALLOCATIONS. In various examples, the inputs to the Data Plane Dimensioning block 204 comprise Traffic forecasts and Configurations). In various examples, the Data Plane Dimensioning functions include Efficiency Calculations, Determination of Resource Utilization, and Performance Calculations. Further, it is seen that from Data Plane Dimensioning block 204, a test 206 (“Low Performance?”) is carried out. If there is not low performance, then a determination 208 (“Pass”) is made. On the other hand, if there is low performance, then a determination 210 (“Trigger New Capacity”) is made. Further still, it is seen that Data Plane Dimensioning block 204 calculates the necessary inputs to the Control Plane Dimensioning block 212.

Still referring to FIG. 2A, it is seen that Control Plane Dimensioning block 212 receives the various inputs from the Data Plane Dimensioning block 204. In various examples, the Control Plane functions include determination of Resource Utilization. Further, it is seen that from Control Plane Dimensioning block 212, a test 214 (“High Utilization?”) is carried out. If there is not high utilization, then a determination 216 (“Pass”) is made. On the other hand, if there is high utilization, then the determination 210 (“Trigger New Capacity”) is made.

Referring now to FIG. 2B, this is a block diagram illustrating an example, non-limiting embodiment of key training features in accordance with various aspects described herein. In various embodiments, the training features should be carefully selected to assure prediction accuracy. As seen in this figure, the key training features (which can be input to Machine Learning (ML) algorithms 250) can comprise:

Bandwidth (Downlink & Uplink):

-   For a given cell, how much total resource in MHz is available for     allocation. For TDD, the effective bandwidth for DL and UL depends     on the TDD DL-UL slot configuration.

Spectral efficiency (Downlink and Uplink):

-   How effectively the data can be transmitted, measured in bps/Hz -   If efficiency values are not available, they can be replaced with     the metrics representing RF conditions, such as DL CQI (Channel     Quality Indicator) values or UL SINR (Signal to     Interference-plus-Noise Ratio) values.

PRB (Physical Resource Block) utilization (Downlink &Uplink):

-   Data plane resource utilization in the DL/UL direction

Traffic of voice and/or other small payload services, e.g., IoT (Internet-Of-Things) devices, etc. (Downlink & Uplink):

-   The volume of voice/IoT traffic (voice/IoT is treated differently     than packet data in the system/network)

Number of users:

-   Average number of connected users in the system/network

Still referring to FIG. 2B, it is seen that an output from Machine Learning (ML) algorithms 250 can comprise PDCCH utilization (e.g., fraction of PDCCH capacity that is being utilized).

Still referring to FIG. 2B, in one example, one model can cover all bandwidths (and/or all technologies)

Still referring to FIG. 2B, in one example, one model can be trained per individual technology (e.g., for LTE and NR respectively). In other examples, even within one technology (e.g., NR), one model can be trained for each frequency band or for each band using different subcarrier spacing (SCS). In one specific example, C-band (using SCS=30 KHz) and mmWave (using SCS=120 KHz) can be trained separately. In another specific example 7 mm band and 8 mm band can be trained separately.

Still referring to FIG. 2B, in one example, a channel quality indicator (which can approximate download channel quality) can be an input into (and/or taken into account by) the ML algorithms.

Still referring to FIG. 2B, in one example, efficiency values (such as defined by 3GPP) can indicate how many bits can be effectively transmitted per unit time and per unit bandwidth and can be an input into (and/or taken into account by) the ML algorithms.

Still referring to FIG. 2B, in one example, MCS (Modulation and Coding Scheme) can be determined based on CQI for a given user and can be an input into (and/or taken into account by) the ML algorithms.

Still referring to FIG. 2B, in one example, MIMO (Multiple Input Multiple Output) using 2 data streams (which can double efficiency) can be an input into (and/or taken into account by) the ML algorithms. In one specific example, rank information associated with MIMO can be an input into (and/or taken into account by) the ML algorithms.

Still referring to FIG. 2B, in one example, PRB utilization for downlink and uplink, separately, can be an input into (and/or taken into account by) the ML algorithms.

Still referring to FIG. 2B, in one example, an amount of voice and/or IoT traffic can be an input into (and/or taken into account by) the ML algorithms. Such IoT data payloads are typically very small; however, they could utilize a lot of control plane resources. Similarly, such voice data payloads are typically very small; however, they could utilize a lot of control plane resources. These IoT and voice profiles are typically different from large data-centric applications (e.g., when a user downloads a file, when a user is web browsing).

Still referring to FIG. 2B, in one example, spectral efficiency can be used when RF metrics (e.g., SINR) are not available. In another example, RF metrics (e.g., SINR) can be used when spectral efficiency is not available. In another example, RF metrics (e.g., SINR) and spectral efficiency can be used.

Referring now to FIGS. 2C-2D, these are block diagrams illustrating example, non-limiting embodiments directed to model & prediction accuracy in accordance with various aspects described herein. In this regard, different machine learning models/algorithms can be utilized to predict the outcome. For example (see FIGS. 2C-2D), a Random Forest algorithm can predict the PDCCH utilization with an R-squared value of higher than 0.9, and an Root Mean Squared Error (RMSE) of less than 0.05, which corresponds to an absolute error of 5%. It is believed that similar results can be achieved using the same training features, if more complicated machine learning algorithms (such as Multiple-layer Perceptron (MLP) Neural Network) are used. Further, it is believed that other machine learning algorithms can perform well as long as the training features are properly selected.

Referring now to FIGS. 2E-2F, these are block diagrams illustrating example, non-limiting embodiments directed to determining threshold for control plane congestion in accordance with various aspects described herein. In one example, to determine when new capacity is needed, we can look into system performance when control plane utilization is high. As seen, a characteristic for control plane congestion is that the data plane resource becomes under-utilized. Various embodiments plot the data plane resource utilization vs. control plane utilization from field data, and search for the turning point where the control plane utilization keeps increasing while the data plane utilization starts to decrease (these FIGS. 2E-2F show how to empirically determine the overload threshold for the control plane). In various examples, turning point can be 60%, 70%, 80%, or 90%.

As described herein, various embodiments can be applied in the context of FDD (Frequency Division Duplex) communications and/or TDD (Time Division Duplex) communications.

As described herein, various embodiments can take account of the control plane in the capacity planning process. Machine learning algorithms can be used to predict the utilization of the control plane. It is shown herein that when the features are properly selected, the accuracy of the model can be very high.

As described herein, various embodiments can account for the control plane (together with the data plane) in the capacity planning process, thus making the planning more comprehensive.

As described herein, various embodiments can provide for an architecture accounting for the interaction between both the control plane and the data plane.

As described herein, various embodiments can provide for machine learning algorithms that are used to predict the utilization of the control plane. It is shown herein that when the features are properly selected, the accuracy of the model can be sufficiently high.

As described herein, various embodiments can provide for a capacity planning tool that is rendered more comprehensive and more accurate by considering both the data plane and control plane resource utilization.

As described herein, various embodiments can provide for well-planned utilization of wireless spectrum (which is an expensive resource for a wireless carrier).

As described herein, various embodiments can help reduce (or eliminate) insufficient spectrum provisioning (such insufficient spectrum provisioning can degrade network performance as well as quality of service, impacting customer satisfaction and consequently increasing churn rate). In various examples, mechanisms can be provided to dimension the spectrum in a manner so as to ensure customer satisfaction

As described herein, various embodiments can help reduce (or eliminate) overprovisioning (such overprovisioning can be unaffordable due, for example, to excessive costs). In various examples, mechanisms can be provided to facilitate accurate prediction of spectrum needs on a periodic (e.g., monthly) basis, and therefore assist in making decisions (e.g., financial decisions, technical decisions) regarding network deployment.

As described herein, various embodiments can consider both the control plane resource allocation and the data plane resource allocation in connection with a network planning process (this can result, for example, in a guarantee of network performance and/or an improvement in network performance).

As described herein, one of the challenges related to control plane dimensioning is that it depends on many factors and therefore has traditionally been more difficult to model than the data plane. Various embodiments can address this via use of machine learning algorithms (wherein, for example, features that are essential in determining the utilization of the control plane channel are properly selected).

As described herein, various embodiments can provide a mechanism to determine when to add network capacity by taking into account control plane utilization.

As described herein, various embodiments can provide one machine learning algorithm per individual technology (e.g., for LTE and NR respectively). In other examples, even within one technology (e.g., NR), one machine learning algorithm can be associated with each frequency band or associated with each band using different subcarrier spacing (SCS). In one specific example, C-band (using SCS=30 KHz) and mmWave (using SCS=120 KHz) can each be associated with a dedicated machine learning algorithm. In another specific example, each of 7 mm band and 8 mm band can be associated with a dedicated machine learning algorithm.

As described herein, various embodiments can be utilized by wireless operators, ISP providers, and/or cloud service providers.

Referring now to FIG. 2G, various steps of a method 2000 according to an embodiment are shown. As seen in this FIG. 2G, step 2001 comprises obtaining first information indicative of data plane utilization, wherein the data plane is associated with a wireless communications network. Next, step 2003 comprises obtaining second information indicative of control plane utilization, wherein the control plane is associated with the wireless communications network. Next, step 2005 comprises applying the first information and the second information to one or more machine learning algorithms. Next, step 2007 comprises generating via the one or more machine learning algorithms one or more outputs, wherein the one or more outputs indicates whether one or more network resources should be added to improve the data plane utilization.

While for purposes of simplicity of explanation, the respective processes are shown and described as a series of blocks in FIG. 2G, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Moreover, not all illustrated blocks may be required to implement the methods described herein.

Referring now to FIG. 2H, various steps of a method 2100 according to an embodiment are shown. As seen in this FIG. 2H, step 2101 comprises receiving first data associated with a first frequency band of a wireless network, wherein the first data characterizes utilization of a data plane of the wireless network at the first frequency band. Next, step 2103 comprises receiving second data associated with a second frequency band of the wireless network, wherein the second data characterizes utilization of the data plane of the wireless network at the second frequency band, and wherein the second frequency band is different from the first frequency band. Next, step 2105 comprises receiving third data associated with the first frequency band of the wireless network, wherein the third data characterizes utilization of a control plane of the wireless network at the first frequency band. Next, step 2107 comprises receiving fourth data associated with the second frequency band of the wireless network, wherein the fourth data characterizes utilization of the control plane of the wireless network at the second frequency band. Next, step 2109 comprises applying the first data and the third data to a first machine learning algorithm. Next, step 2111 comprises generating via the first machine learning algorithm one or more first outputs, wherein the one or more first outputs indicates whether one or more first network resources associated with the first frequency band should be adjusted to improve data plane utilization at the first frequency band. Next, step 2113 comprises applying the second data and the fourth data to a second machine learning algorithm, wherein the second machine learning algorithm is different from the first machine learning algorithm. Next, step 2115 comprises generating via the second machine learning algorithm one or more second outputs, wherein the one or more second outputs indicates whether one or more second network resources associated with the second frequency band should be adjusted to improve data plane utilization at the second frequency band.

While for purposes of simplicity of explanation, the respective processes are shown and described as a series of blocks in FIG. 2H, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Moreover, not all illustrated blocks may be required to implement the methods described herein.

Referring now to FIG. 2I, various steps of a method 2200 according to an embodiment are shown. As seen in this FIG. 2I, step 2201 comprises obtaining, by a processing system including a processor, first training data associated with a first frequency band of a wireless network, wherein the first training data characterizes utilization of a data plane of the wireless network at the first frequency band. Next, step 2203 comprises obtaining, by the processing system, second training data associated with a second frequency band of the wireless network, wherein the second training data characterizes utilization of the data plane of the wireless network at the second frequency band, and wherein the second frequency band is distinct from the first frequency band. Next, step 2205 comprises obtaining, by the processing system, third training data associated with the first frequency band of the wireless network, wherein the third training data characterizes utilization of a control plane of the wireless network at the first frequency band. Next, step 2207 comprises obtaining, by the processing system, fourth training data associated with the second frequency band of the wireless network, wherein the fourth training data characterizes utilization of the control plane of the wireless network at the second frequency band. Next, step 2209 comprises utilizing, by the processing system, the first training data and the third training data to train a first machine learning algorithm that is associated with the first frequency band. Next, step 2211 comprises utilizing, by the processing system, the second training data and the fourth training data to train a second machine learning algorithm that is associated with the second frequency band, wherein the second machine learning algorithm is distinct from the first machine learning algorithm.

While for purposes of simplicity of explanation, the respective processes are shown and described as a series of blocks in FIG. 2I, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Moreover, not all illustrated blocks may be required to implement the methods described herein.

Referring now to FIG. 3 , a block diagram 300 is shown illustrating an example, non-limiting embodiment of a virtualized communication network in accordance with various aspects described herein. In particular a virtualized communication network is presented that can be used to implement some or all of the subsystems and functions of system 100, some or all of the subsystems and functions of system 200, some or all of the subsystems and functions of system 250, and/or some or all of the functions of methods 2000, 2100, 2200. For example, virtualized communication network 300 can facilitate in whole or in part network planning (and/or configuration) mechanisms that are based upon both data plane utilization and control plane utilization.

In particular, a cloud networking architecture is shown that leverages cloud technologies and supports rapid innovation and scalability via a transport layer 350, a virtualized network function cloud 325 and/or one or more cloud computing environments 375. In various embodiments, this cloud networking architecture is an open architecture that leverages application programming interfaces (APIs); reduces complexity from services and operations; supports more nimble business models; and rapidly and seamlessly scales to meet evolving customer requirements including traffic growth, diversity of traffic types, and diversity of performance and reliability expectations.

In contrast to traditional network elements - which are typically integrated to perform a single function, the virtualized communication network employs virtual network elements (VNEs) 330, 332, 334, etc. that perform some or all of the functions of network elements 150, 152, 154, 156, etc. For example, the network architecture can provide a substrate of networking capability, often called Network Function Virtualization Infrastructure (NFVI) or simply infrastructure that is capable of being directed with software and Software Defined Networking (SDN) protocols to perform a broad variety of network functions and services. This infrastructure can include several types of substrates. The most typical type of substrate being servers that support Network Function Virtualization (NFV), followed by packet forwarding capabilities based on generic computing resources, with specialized network technologies brought to bear when general purpose processors or general purpose integrated circuit devices offered by merchants (referred to herein as merchant silicon) are not appropriate. In this case, communication services can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1 ), such as an edge router can be implemented via a VNE 330 composed of NFV software modules, merchant silicon, and associated controllers. The software can be written so that increasing workload consumes incremental resources from a common resource pool, and moreover so that it’s elastic: so the resources are only consumed when needed. In a similar fashion, other network elements such as other routers, switches, edge caches, and middle-boxes are instantiated from the common resource pool. Such sharing of infrastructure across a broad set of uses makes planning and growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wired and/or wireless transport elements, network elements and interfaces to provide broadband access 110, wireless access 120, voice access 130, media access 140 and/or access to content sources 175 for distribution of content to any or all of the access technologies. In particular, in some cases a network element needs to be positioned at a specific place, and this allows for less sharing of common infrastructure. Other times, the network elements have specific physical layer adapters that cannot be abstracted or virtualized, and might require special DSP code and analog front-ends (AFEs) that do not lend themselves to implementation as VNEs 330, 332 or 334. These network elements can be included in transport layer 350.

The virtualized network function cloud 325 interfaces with the transport layer 350 to provide the VNEs 330, 332, 334, etc. to provide specific NFVs. In particular, the virtualized network function cloud 325 leverages cloud operations, applications, and architectures to support networking workloads. The virtualized network elements 330, 332 and 334 can employ network function software that provides either a one-for-one mapping of traditional network element function or alternately some combination of network functions designed for cloud computing. For example, VNEs 330, 332 and 334 can include route reflectors, domain name system (DNS) servers, and dynamic host configuration protocol (DHCP) servers, system architecture evolution (SAE) and/or mobility management entity (MME) gateways, broadband network gateways, IP edge routers for IP-VPN, Ethernet and other services, load balancers, distributers and other network elements. Because these elements don’t typically need to forward large amounts of traffic, their workload can be distributed across a number of servers - each of which adds a portion of the capability, and overall which creates an elastic function with higher availability than its former monolithic version. These virtual network elements 330, 332, 334, etc. can be instantiated and managed using an orchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualized network function cloud 325 via APIs that expose functional capabilities of the VNEs 330, 332, 334, etc. to provide the flexible and expanded capabilities to the virtualized network function cloud 325. In particular, network workloads may have applications distributed across the virtualized network function cloud 325 and cloud computing environment 375 and in the commercial cloud, or might simply orchestrate workloads supported entirely in NFV infrastructure from these third party locations.

Turning now to FIG. 4 , there is illustrated a block diagram of a computing environment in accordance with various aspects described herein. In order to provide additional context for various embodiments of the embodiments described herein, FIG. 4 and the following discussion are intended to provide a brief, general description of a suitable computing environment 400 in which the various embodiments of the subject disclosure can be implemented. In particular, computing environment 400 can be used in the implementation of network elements 150, 152, 154, 156, access terminal 112, base station or access point 122, switching device 132, media terminal 142, and/or VNEs 330, 332, 334, etc. Each of these devices can be implemented via computer-executable instructions that can run on one or more computers, and/or in combination with other program modules and/or as a combination of hardware and software. For example, computing environment 400 can facilitate in whole or in part network planning (and/or configuration) mechanisms that are based upon both data plane utilization and control plane utilization.

Generally, program modules comprise routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods can be practiced with other computer system configurations, comprising single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors as well as other application specific circuits such as an application specific integrated circuit, digital logic circuit, state machine, programmable gate array or other circuit that processes input signals or data and that produces output signals or data in response thereto. It should be noted that while any functions and features described herein in association with the operation of a processor could likewise be performed by a processing circuit.

The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which can comprise computer-readable storage media and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media can be any available storage media that can be accessed by the computer and comprises both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable instructions, program modules, structured data or unstructured data.

Computer-readable storage media can comprise, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM),flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.

Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and comprises any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media comprise wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 4 , the example environment can comprise a computer 402, the computer 402 comprising a processing unit 404, a system memory 406 and a system bus 408. The system bus 408 couples system components including, but not limited to, the system memory 406 to the processing unit 404. The processing unit 404 can be any of various commercially available processors. Dual microprocessors and other multiprocessor architectures can also be employed as the processing unit 404.

The system bus 408 can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 406 comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer 402, such as during startup. The RAM 412 can also comprise a high-speed RAM such as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414 (e.g., EIDE, SATA), which internal HDD 414 can also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD) 416, (e.g., to read from or write to a removable diskette 418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or, to read from or write to other high capacity optical media such as the DVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can be connected to the system bus 408 by a hard disk drive interface 424, a magnetic disk drive interface 426 and an optical drive interface 428, respectively. The hard disk drive interface 424 for external drive implementations comprises at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer 402, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to a hard disk drive (HDD), a removable magnetic diskette, and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, can also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein.

A number of program modules can be stored in the drives and RAM 412, comprising an operating system 430, one or more application programs 432, other program modules 434 and program data 436. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM 412. The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems.

A user can enter commands and information into the computer 402 through one or more wired/wireless input devices, e.g., a keyboard 438 and a pointing device, such as a mouse 440. Other input devices (not shown) can comprise a microphone, an infrared (IR) remote control, a joystick, a game pad, a stylus pen, touch screen or the like. These and other input devices are often connected to the processing unit 404 through an input device interface 442 that can be coupled to the system bus 408, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a universal serial bus (USB) port, an IR interface, etc.

A monitor 444 or other type of display device can be also connected to the system bus 408 via an interface, such as a video adapter 446. It will also be appreciated that in alternative embodiments, a monitor 444 can also be any display device (e.g., another computer having a display, a smart phone, a tablet computer, etc.) for receiving display information associated with computer 402 via any communication means, including via the Internet and cloud-based networks. In addition to the monitor 444, a computer typically comprises other peripheral output devices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s) 448. The remote computer(s) 448 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically comprises many or all of the elements described relative to the computer 402, although, for purposes of brevity, only a remote memory/storage device 450 is illustrated. The logical connections depicted comprise wired/wireless connectivity to a local area network (LAN) 452 and/or larger networks, e.g., a wide area network (WAN) 454. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 402 can be connected to the LAN 452 through a wired and/or wireless communication network interface or adapter 456. The adapter 456 can facilitate wired or wireless communication to the LAN 452, which can also comprise a wireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprise a modem 458 or can be connected to a communications server on the WAN 454 or has other means for establishing communications over the WAN 454, such as by way of the Internet. The modem 458, which can be internal or external and a wired or wireless device, can be connected to the system bus 408 via the input device interface 442. In a networked environment, program modules depicted relative to the computer 402 or portions thereof, can be stored in the remote memory/storage device 450. It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used.

The computer 402 can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This can comprise Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bed in a hotel room or a conference room at work, without wires. Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands for example or with products that contain both bands (dual band), so the networks can provide real-world performance similar to the basic 10BaseT wired Ethernet networks used in many offices.

Turning now to FIG. 5 , an embodiment 500 of a mobile network platform 510 is shown that is an example of network elements 150, 152, 154, 156, and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitate in whole or in part network planning (and/or configuration) mechanisms that are based upon both data plane utilization and control plane utilization. In one or more embodiments, the mobile network platform 510 can generate and receive signals transmitted and received by base stations or access points such as base station or access point 122. Generally, mobile network platform 510 can comprise components, e.g., nodes, gateways, interfaces, servers, or disparate platforms, that facilitate both packet-switched (PS) (e.g., internet protocol (IP), frame relay, asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic (e.g., voice and data), as well as control generation for networked wireless telecommunication. As a non-limiting example, mobile network platform 510 can be included in telecommunications carrier networks, and can be considered carrier-side components as discussed elsewhere herein. Mobile network platform 510 comprises CS gateway node(s) 512 which can interface CS traffic received from legacy networks like telephony network(s) 540 (e.g., public switched telephone network (PSTN), or public land mobile network (PLMN)) or a signaling system #7 (SS7) network 560. CS gateway node(s) 512 can authorize and authenticate traffic (e.g., voice) arising from such networks. Additionally, CS gateway node(s) 512 can access mobility, or roaming, data generated through SS7 network 560; for instance, mobility data stored in a visited location register (VLR), which can reside in memory 530. Moreover, CS gateway node(s) 512 interfaces CS-based traffic and signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTS network, CS gateway node(s) 512 can be realized at least in part in gateway GPRS support node(s) (GGSN). It should be appreciated that functionality and specific operation of CS gateway node(s) 512, PS gateway node(s) 518, and serving node(s) 516, is provided and dictated by radio technology(ies) utilized by mobile network platform 510 for telecommunication over a radio access network 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic and signaling, PS gateway node(s) 518 can authorize and authenticate PS-based data sessions with served mobile devices. Data sessions can comprise traffic, or content(s), exchanged with networks external to the mobile network platform 510, like wide area network(s) (WANs) 550, enterprise network(s) 570, and service network(s) 580, which can be embodied in local area network(s) (LANs), can also be interfaced with mobile network platform 510 through PS gateway node(s) 518. It is to be noted that WANs 550 and enterprise network(s) 570 can embody, at least in part, a service network(s) like IP multimedia subsystem (IMS). Based on radio technology layer(s) available in technology resource(s) or radio access network 520, PS gateway node(s) 518 can generate packet data protocol contexts when a data session is established; other data structures that facilitate routing of packetized data also can be generated. To that end, in an aspect, PS gateway node(s) 518 can comprise a tunnel interface (e.g., tunnel termination gateway (TTG) in 3GPP UMTS network(s) (not shown)) which can facilitate packetized communication with disparate wireless network(s), such as Wi-Fi networks.

In embodiment 500, mobile network platform 510 also comprises serving node(s) 516 that, based upon available radio technology layer(s) within technology resource(s) in the radio access network 520, convey the various packetized flows of data streams received through PS gateway node(s) 518. It is to be noted that for technology resource(s) that rely primarily on CS communication, server node(s) can deliver traffic without reliance on PS gateway node(s) 518; for example, server node(s) can embody at least in part a mobile switching center. As an example, in a 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s) 514 in mobile network platform 510 can execute numerous applications that can generate multiple disparate packetized data streams or flows, and manage (e.g., schedule, queue, format ...) such flows. Such application(s) can comprise add-on features to standard services (for example, provisioning, billing, customer support ...) provided by mobile network platform 510. Data streams (e.g., content(s) that are part of a voice call or data session) can be conveyed to PS gateway node(s) 518 for authorization/authentication and initiation of a data session, and to serving node(s) 516 for communication thereafter. In addition to application server, server(s) 514 can comprise utility server(s), a utility server can comprise a provisioning server, an operations and maintenance server, a security server that can implement at least in part a certificate authority and firewalls as well as other security mechanisms, and the like. In an aspect, security server(s) secure communication served through mobile network platform 510 to ensure network’s operation and data integrity in addition to authorization and authentication procedures that CS gateway node(s) 512 and PS gateway node(s) 518 can enact. Moreover, provisioning server(s) can provision services from external network(s) like networks operated by a disparate service provider; for instance, WAN 550 or Global Positioning System (GPS) network(s) (not shown). Provisioning server(s) can also provision coverage through networks associated to mobile network platform 510 (e.g., deployed and operated by the same service provider), such as the distributed antennas networks shown in FIG. 1 that enhance wireless service coverage by providing more network coverage.

It is to be noted that server(s) 514 can comprise one or more processors configured to confer at least in part the functionality of mobile network platform 510. To that end, the one or more processor can execute code instructions stored in memory 530, for example. It is should be appreciated that server(s) 514 can comprise a content manager, which operates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related to operation of mobile network platform 510. Other operational information can comprise provisioning information of mobile devices served through mobile network platform 510, subscriber databases; application intelligence, pricing schemes, e.g., promotional rates, flat-rate programs, couponing campaigns; technical specification(s) consistent with telecommunication protocols for operation of disparate radio, or wireless, technology layers; and so forth. Memory 530 can also store information from at least one of telephony network(s) 540, WAN 550, SS7 network 560, or enterprise network(s) 570. In an aspect, memory 530 can be, for example, accessed as part of a data store component or as a remotely connected memory store.

In order to provide a context for the various aspects of the disclosed subject matter, FIG. 5 , and the following discussion, are intended to provide a brief, general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented. While the subject matter has been described above in the general context of computer-executable instructions of a computer program that runs on a computer and/or computers, those skilled in the art will recognize that the disclosed subject matter also can be implemented in combination with other program modules. Generally, program modules comprise routines, programs, components, data structures, etc. that perform particular tasks and/or implement particular abstract data types.

Turning now to FIG. 6 , an illustrative embodiment of a communication device 600 is shown. The communication device 600 can serve as an illustrative embodiment of devices such as data terminals 114, mobile devices 124, vehicle 126, display devices 144 or other client devices for communication via either communications network 125. For example, computing device 600 can facilitate in whole or in part network planning (and/or configuration) mechanisms that are based upon both data plane utilization and control plane utilization.

The communication device 600 can comprise a wireline and/or wireless transceiver 602 (herein transceiver 602), a user interface (UI) 604, a power supply 614, a location receiver 616, a motion sensor 618, an orientation sensor 620, and a controller 606 for managing operations thereof. The transceiver 602 can support short-range or long-range wireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, or cellular communication technologies, just to mention a few (Bluetooth® and ZigBee® are trademarks registered by the Bluetooth® Special Interest Group and the ZigBee® Alliance, respectively). Cellular technologies can include, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next generation wireless communication technologies as they arise. The transceiver 602 can also be adapted to support circuit-switched wireline access technologies (such as PSTN), packet-switched wireline access technologies (such as TCP/IP, VoIP, etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 with a navigation mechanism such as a roller ball, a joystick, a mouse, or a navigation disk for manipulating operations of the communication device 600. The keypad 608 can be an integral part of a housing assembly of the communication device 600 or an independent device operably coupled thereto by a tethered wireline interface (such as a USB cable) or a wireless interface supporting for example Bluetooth®. The keypad 608 can represent a numeric keypad commonly used by phones, and/or a QWERTY keypad with alphanumeric keys. The UI 604 can further include a display 610 such as monochrome or color LCD (Liquid Crystal Display), OLED (Organic Light Emitting Diode) or other suitable display technology for conveying images to an end user of the communication device 600. In an embodiment where the display 610 is touch-sensitive, a portion or all of the keypad 608 can be presented by way of the display 610 with navigation features.

The display 610 can use touch screen technology to also serve as a user interface for detecting user input. As a touch screen display, the communication device 600 can be adapted to present a user interface having graphical user interface (GUI) elements that can be selected by a user with a touch of a finger. The display 610 can be equipped with capacitive, resistive or other forms of sensing technology to detect how much surface area of a user’s finger has been placed on a portion of the touch screen display. This sensing information can be used to control the manipulation of the GUI elements or other functions of the user interface. The display 610 can be an integral part of the housing assembly of the communication device 600 or an independent device communicatively coupled thereto by a tethered wireline interface (such as a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audio technology for conveying low volume audio (such as audio heard in proximity of a human ear) and high volume audio (such as speakerphone for hands free operation). The audio system 612 can further include a microphone for receiving audible signals of an end user. The audio system 612 can also be used for voice recognition applications. The UI 604 can further include an image sensor 613 such as a charged coupled device (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologies such as replaceable and rechargeable batteries, supply regulation technologies, and/or charging system technologies for supplying energy to the components of the communication device 600 to facilitate long-range or short-range portable communications. Alternatively, or in combination, the charging system can utilize external power sources such as DC power supplied over a physical interface such as a USB port or other suitable tethering technologies.

The location receiver 616 can utilize location technology such as a global positioning system (GPS) receiver capable of assisted GPS for identifying a location of the communication device 600 based on signals generated by a constellation of GPS satellites, which can be used for facilitating location services such as navigation. The motion sensor 618 can utilize motion sensing technology such as an accelerometer, a gyroscope, or other suitable motion sensing technology to detect motion of the communication device 600 in three-dimensional space. The orientation sensor 620 can utilize orientation sensing technology such as a magnetometer to detect the orientation of the communication device 600 (north, south, west, and east, as well as combined orientations in degrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to also determine a proximity to a cellular, WiFi, Bluetooth®, or other wireless access points by sensing techniques such as utilizing a received signal strength indicator (RSSI) and/or signal time of arrival (TOA) or time of flight (TOF) measurements. The controller 606 can utilize computing technologies such as a microprocessor, a digital signal processor (DSP), programmable gate arrays, application specific integrated circuits, and/or a video processor with associated storage memory such as Flash, ROM, RAM, SRAM, DRAM or other storage technologies for executing computer instructions, controlling, and processing data supplied by the aforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or more embodiments of the subject disclosure. For instance, the communication device 600 can include a slot for adding or removing an identity module such as a Subscriber Identity Module (SIM) card or Universal Integrated Circuit Card (UICC). SIM or UICC cards can be used for identifying subscriber services, executing programs, storing subscriber data, and so on.

The terms “first,” “second,” “third,” and so forth, as used in the claims, unless otherwise clear by context, is for clarity only and doesn’t otherwise indicate or imply any order in time. For instance, “a first determination,” “a second determination,” and “a third determination,” does not indicate or imply that the first determination is to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components described herein can be either volatile memory or nonvolatile memory, or can comprise both volatile and nonvolatile memory, by way of illustration, and not limitation, volatile memory, non-volatile memory, disk storage, and memory storage. Further, nonvolatile memory can be included in read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can comprise random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the disclosed memory components of systems or methods herein are intended to comprise, without being limited to comprising, these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can be practiced with other computer system configurations, comprising single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as personal computers, hand-held computing devices (e.g., PDA, phone, smartphone, watch, tablet computers, netbook computers, etc.), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network; however, some if not all aspects of the subject disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can be generated including services being accessed, media consumption history, user preferences, and so forth. This information can be obtained by various methods including user input, detecting types of communications (e.g., video content vs. audio content), analysis of content streams, sampling, and so forth. The generating, obtaining and/or monitoring of this information can be responsive to an authorization provided by the user. In one or more embodiments, an analysis of data can be subject to authorization from user(s) associated with the data, such as an opt-in, an opt-out, acknowledgement requirements, notifications, selective authorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificial intelligence (AI) to facilitate automating one or more features described herein. The embodiments (e.g., in connection with automatically planning (and/or configuring) based upon both data plane utilization and control plane utilization) can employ various AIbased schemes for carrying out various embodiments thereof. Moreover, the classifier can be employed to determine a ranking or priority of various plans and/or configurations. A classifier is a function that maps an input attribute vector, x = (x1, x2, x3, x4, ..., xn), to a confidence that the input belongs to a class, that is, f(x) = confidence (class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to determine or infer an action that a user desires to be automatically performed. A support vector machine (SVM) is an example of a classifier that can be employed. The SVM operates by finding a hypersurface in the space of possible inputs, which the hypersurface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches comprise, e.g., naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing UE behavior, operator preferences, historical information, receiving extrinsic information). For example, SVMs can be configured via a learning or training phase within a classifier constructor and feature selection module. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to predetermined criteria various planning (and/or configuring) states.

As used in some contexts in this application, in some embodiments, the terms “component,” “system” and the like are intended to refer to, or comprise, a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution. As an example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confers at least in part the functionality of the electronic components. While various components have been illustrated as separate components, it will be appreciated that multiple components can be implemented as a single component, or a single component can be implemented as multiple components, without departing from example embodiments.

Further, the various embodiments can be implemented as a method, apparatus or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device or computer-readable storage/communications media. For example, computer readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g., compact disk (CD), digital versatile disk (DVD)), smart cards, and flash memory devices (e.g., card, stick, key drive). Of course, those skilled in the art will recognize many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to mean serving as an instance or illustration. Any embodiment or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word example or exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,” subscriber station,” “access terminal,” “terminal,” “handset,” “mobile device” (and/or terms representing similar terminology) can refer to a wireless device utilized by a subscriber or user of a wireless communication service to receive or convey data, control, voice, video, sound, gaming or substantially any data-stream or signaling-stream. The foregoing terms are utilized interchangeably herein and with reference to the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” and the like are employed interchangeably throughout, unless context warrants particular distinctions among the terms. It should be appreciated that such terms can refer to human entities or automated components supported through artificial intelligence (e.g., a capacity to make inference based, at least, on complex mathematical formalisms), which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to comprising, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. Processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor can also be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components or computer-readable storage media, described herein can be either volatile memory or nonvolatile memory or can include both volatile and nonvolatile memory.

What has been described above includes mere examples of various embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing these examples, but one of ordinary skill in the art can recognize that many further combinations and permutations of the present embodiments are possible. Accordingly, the embodiments disclosed and/or claimed herein are intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.

In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via one or more intervening items. Such items and intervening items include, but are not limited to, junctions, communication paths, components, circuit elements, circuits, functional blocks, and/or devices. As an example of indirect coupling, a signal conveyed from a first item to a second item may be modified by one or more intervening items by modifying the form, nature or format of information in a signal, while one or more elements of the information in the signal are nevertheless conveyed in a manner than can be recognized by the second item. In a further example of indirect coupling, an action in a first item can cause a reaction on the second item, as a result of actions and/or reactions in one or more intervening items.

Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement which achieves the same or similar purpose may be substituted for the embodiments described or shown by the subject disclosure. The subject disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, can be used in the subject disclosure. For instance, one or more features from one or more embodiments can be combined with one or more features of one or more other embodiments. In one or more embodiments, features that are positively recited can also be negatively recited and excluded from the embodiment with or without replacement by another structural and/or functional feature. The steps or functions described with respect to the embodiments of the subject disclosure can be performed in any order. The steps or functions described with respect to the embodiments of the subject disclosure can be performed alone or in combination with other steps or functions of the subject disclosure, as well as from other embodiments or from other steps that have not been described in the subject disclosure. Further, more than or less than all of the features described with respect to an embodiment can also be utilized. 

What is claimed is:
 1. A device comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining first information indicative of data plane utilization, wherein the data plane is associated with a wireless communications network; obtaining second information indicative of control plane utilization, wherein the control plane is associated with the wireless communications network; applying the first information and the second information to one or more machine learning algorithms; and generating via the one or more machine learning algorithms one or more outputs, wherein the one or more outputs indicates whether one or more network resources should be added to improve the data plane utilization.
 2. The device of claim 1, wherein the first information comprises uplink information associated with the data plane and downlink information associated with the data plane.
 3. The device of claim 2, wherein the uplink information associated with the data plane characterizes uplink data flow for a plurality of end user devices that communicate with the wireless communications network and wherein the downlink information associated with the data plane characterizes downlink data flow for the plurality of end user devices that communicate with the wireless communications network.
 4. The device of claim 2, wherein the uplink information associated with the data plane is obtained from one or more servers of the wireless communications network and the downlink information associated with the data plane is obtained from the one or more servers of the wireless communications network.
 5. The device of claim 1, wherein the second information characterizes control information for a plurality of end user devices that communicate with the wireless communications network.
 6. The device of claim 5, wherein the control information is obtained from one or more servers of the wireless communications network.
 7. The device of claim 1, wherein: the wireless communications network supports a plurality of frequency bands, the plurality of frequency bands comprise at least a first frequency band and a second frequency band, and the first frequency band is different from the second frequency band; the first information comprises first data plane statistics associated with the first frequency band and second data plane statistics associated with the second frequency band; the second information comprises first control plane statistics associated with the first frequency band and second control plane statistics associated with the second frequency band; the one or more machine learning algorithms comprises a plurality of machine learning algorithms, the plurality of machine learning algorithms comprises at least a first machine learning algorithm and a second machine learning algorithm, and the first machine learning algorithm is different from the second machine learning algorithm; the first data plane statistics and the first control plane statistics are applied to the first machine learning algorithm; the first machine learning algorithm generates a first output that indicates whether one or more first network resources should be added to improve the data plane utilization for the first frequency band; the second data plane statistics and the second control plane statistics are applied to the second machine learning algorithm; and the second machine learning algorithm generates a second output that indicates whether one or more second network resources should be added to improve the data plane utilization for the second frequency band.
 8. The device of claim 1, wherein the operations further comprise: responsive to the one or more outputs, facilitating addition of the one or more network resources to improve the data plane utilization.
 9. The device of claim 8, wherein the addition of the one or more network resources is performed in real-time or in near real-time.
 10. The device of claim 1, wherein the operations further comprise: obtaining first training data indicative of data plane utilization during a training period; obtaining second training data indicative of control plane utilization during the training period; utilizing the first training data and the second training data to train the one or more machine learning algorithms.
 11. The device of claim 1, wherein the wireless communications network is configured for bi-directional communication with a plurality of end user devices.
 12. The device of claim 11, wherein each of the plurality of end user devices comprises a cell phone, a smartphone, a tablet computer, a laptop computer, or any combination thereof.
 13. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: receiving first data associated with a first frequency band of a wireless network, wherein the first data characterizes utilization of a data plane of the wireless network at the first frequency band; receiving second data associated with a second frequency band of the wireless network, wherein the second data characterizes utilization of the data plane of the wireless network at the second frequency band, and wherein the second frequency band is different from the first frequency band; receiving third data associated with the first frequency band of the wireless network, wherein the third data characterizes utilization of a control plane of the wireless network at the first frequency band; receiving fourth data associated with the second frequency band of the wireless network, wherein the fourth data characterizes utilization of the control plane of the wireless network at the second frequency band; applying the first data and the third data to a first machine learning algorithm; generating via the first machine learning algorithm one or more first outputs, wherein the one or more first outputs indicates whether one or more first network resources associated with the first frequency band should be adjusted to improve data plane utilization at the first frequency band; applying the second data and the fourth data to a second machine learning algorithm, wherein the second machine learning algorithm is different from the first machine learning algorithm; and generating via the second machine learning algorithm one or more second outputs, wherein the one or more second outputs indicates whether one or more second network resources associated with the second frequency band should be adjusted to improve data plane utilization at the second frequency band.
 14. The non-transitory machine-readable medium of claim 13, wherein: the one or more first network resources associated with the first frequency band are adjusted in real-time or in near real-time; and the one or more second network resources associated with the second frequency band are adjusted in real-time or in near real-time.
 15. The non-transitory machine-readable medium of claim 13, wherein: the one or more first network resources associated with the first frequency band are adjusted by adding first capacity; and the one or more second network resources associated with the second frequency band are adjusted by adding second capacity.
 16. The non-transitory machine-readable medium of claim 13, wherein the wireless network is configured for bi-directional communication with a plurality of mobile devices, and wherein each of the plurality of mobile devices comprises a cell phone, a smartphone, a tablet computer, a laptop computer, or any combination thereof.
 17. A method comprising: obtaining, by a processing system including a processor, first training data associated with a first frequency band of a wireless network, wherein the first training data characterizes utilization of a data plane of the wireless network at the first frequency band; obtaining, by the processing system, second training data associated with a second frequency band of the wireless network, wherein the second training data characterizes utilization of the data plane of the wireless network at the second frequency band, and wherein the second frequency band is distinct from the first frequency band; obtaining, by the processing system, third training data associated with the first frequency band of the wireless network, wherein the third training data characterizes utilization of a control plane of the wireless network at the first frequency band; obtaining, by the processing system, fourth training data associated with the second frequency band of the wireless network, wherein the fourth training data characterizes utilization of the control plane of the wireless network at the second frequency band; utilizing, by the processing system, the first training data and the third training data to train a first machine learning algorithm that is associated with the first frequency band; and utilizing, by the processing system, the second training data and the fourth training data to train a second machine learning algorithm that is associated with the second frequency band, wherein the second machine learning algorithm is distinct from the first machine learning algorithm.
 18. The method of claim 17, further comprising: obtaining, by the processing system, first current data plane statistics associated with the first frequency band and second current data plane statistics associated with the second frequency band; obtaining, by the processing system, first current control plane statistics associated with the first frequency band and second current control plane statistics associated with the second frequency band; applying, by the processing system, the first data plane statistics and the first control plane statistics to the first machine learning algorithm; and applying, by the processing system, the second data plane statistics and the second control plane statistics to the second machine learning algorithm.
 19. The method of claim 17, wherein: the first machine learning algorithm generates a first output that indicates whether one or more first network resources associated with the first frequency band should be added to improve the data plane utilization for the first frequency band; and the second machine learning algorithm generates a second output that indicates whether one or more second network resources associated with the second frequency band should be added to improve the data plane utilization for the second frequency band.
 20. The method of claim 17, wherein the wireless network is configured for bi-directional communication with a plurality of communication devices, and wherein each of the plurality of communication devices comprises a cell phone, a smartphone, a tablet computer, a laptop computer, or any combination thereof. 